System management scheme for a signal-processing-based decision support system

ABSTRACT

A decision support system and a method for a signal processing system includes a video processing system for receiving and processing a video stream and providing a video output; a video quality evaluation module that receives the video output from the video processing system and evaluates the quality according to predetermined criteria; a video optimizer adapted for receiving the evaluated quality of the video output from evaluation module and level settings of parameters and for setting controls of the levels settings of parameters of video processing system, said video optimizer including a Multi Objective Genetic Algorithm (MOGA) engine, wherein the MOGA uses genetic algorithms to optimize the settings of controls for video processing system to optimize image quality at a predetermined level.

The present invention relates to the management of a signal-processingbased support system for Decision Support Systems (DSS). Moreparticularly, the present invention relates to the development of a DSSwhose components are generally non-linear.

When streaming multimedia data over scarce-resource devices (e.g.streaming video/multimedia over wireless networks) there is always atradeoff between quality of the received signal and the associatedcosts. For example, in the case where one chooses to stream video over awireless device, such as a Personal Data Assistant (PDA) of a cell phonewith a G3 configuration, there is the possibility that networkcongestion (which may or may not have been foreseen) and/or a suddendecrease of the available bandwidth might occur. It is also possiblethat the available storage medium might also suddenly decreaseunexpectedly.

Such unexpected variations in bandwidth availability can affect themultimedia stream in a catastrophic fashion. For example, in the case ofvideo conferencing (which may or may not occur on a cell phone) the callcould be dropped due to network congestion.

Should one be downloading multimedia (e.g. purchasing a movie viadownloading to a storage device) any variation in bandwidth thatproduces a drop would cause the session to fail and the user would berequired to restart the session.

In addition, there are many other factors beside image quality thatshould be taken into account, and these factors have not been consideredby the prior art. For example, there are competing factors of powerdissipation, market costs, and time to market versus image quality, asshown in FIG. 1. The relationships between such factors do not alwaysfit into proportions such that a solution can be obtained algebraicallyor easily. However, there is a need for an overall scheme to provide amanagement scheme for a signal-processing-based decision support system.

Accordingly, it is an aspect of the present invention to provide asystem that manages a signal-processing-based decision support system.The decision support system is capable of dynamically deciding the bestallocation of bandwidth to reduce/eliminate the possibility thatmultimedia streaming has catastrophic interrupts, in accordance withother competing factors such as power dissipation, market costs, etc. Inother words, the system automatically decides the best allocation underthe circumstances and adapts to further changes in circumstances.Whether the changed circumstances are due to one or a combination ofnetwork congestion, decrease in bandwidth available, temporary loss of astorage medium, power reduction, etc., the system will automaticallyadjust the parameters of the multimedia stream to preserve the mostimportant aspects thereof. During a conference call, typicallypreserving the clarity of the audio would be more important thandisplaying a slightly fuzzy video, so the re-allocation in thatparticular case can be made with ensuring that audio quality ispreserved at a certain level.

FIG. 1 is a diagram showing a few of the competing factors in a decisionsupport system.

FIG. 2 illustrates a simplified block diagram of the system according toan embodiment of the present invention.

FIG. 3 is a flowchart highlighting an aspect of the present invention.

In the following description, for purposes of explanation rather thanlimitation, specific details are set forth such as the particulararchitecture, interfaces, techniques, etc., in order to provide athorough understanding of the present invention. However, it will beapparent to those skilled in the art that the present invention may bepracticed in other embodiments, which depart from these specificdetails. Moreover, for the purpose of clarity, detailed descriptions ofwell-known devices, circuits, and methods are omitted so as not toobscure the description of the present invention with unnecessarydetail.

DSS for signal-processing systems are unknown heretofore because theconstituent components are generally highly non-linear, the nature oftheir interaction is not well defined and are most likelynon-deterministic. In the modeling process disclosed herein, the signalprocessing system is a complex modeling system that includes structurethat controls the associated level of performance, and the expenseassociated with such systems. Expense associated with these systemscould be broken down into manufacturing costs, level of performance forunit money, time to market, and effect on market share.

FIG. 2 illustrates an aspect of the video decision support systemaccording to the present invention.

An optimizer module 100 comprises a Multi-Objective Genetic Algorithm(MOGA) sub-module 105 and a statistical analyzer 110. The MOGA searchesthe hyper space whose coordinates are the different kinds of expensesand the signal processing parameters. The MOGA enables anon-deterministic search space adequately and permits the finding ofglobal optima without being stuck at local optima from initialindications of genetic algorithm models. Applicant respectfullyincorporates by reference U.S. patent application Ser. No. 09/717,981entitled “System and Method for Optimizing Parameter settings in a chainof Video Processing Algorithms” by Walid S. I. Ali and Cornelis Van Zon,and Ser. No. 09/734,823 entitled “System and Method for providing aScalable Dynamic Objective Metric for automatic Video QualityEvaluation” by Walid S. I. Ali and Cornelis Van Zon as backgroundmaterial regarding the role of genetic algorithms and optimizing controlparameter settings by a multi-objective engine to help formulate bettersearch points to find global optima.

The statistical analyzer 110, which analyzes inputs such as market costs125 and time to market 126, can provide in conjunction with the rest ofthe system provide formal estimates for the relationship between imagequality, manufacturing costs, time to market, power dissipation,bandwidth usage, etc. There can be additional/extra factors (any extrafactors that may need to be or preferably should be analyzed in thisscheme), and they are represented by box 127 (e.g. bandwidth, networkcongestion, etc). Typically, the statistical analyzer will interpolatethe data which it compiles, and such information can be provided to theMOGA for further analysis.

A video-processing system 115 is adapted for receiving a video streaminput. The system includes a picture color improvement module 117 and asharpness enhancement and noise cancellation modules 119. A videoquality evaluation module 121 receives the video from the videoprocessing system 115 and rates the quality (e.g. good, bad and possiblymany variations therebetween) based on predetermined objective criteria.

The optimizer module 100 will initially provide for a level setting ofparameters and controls based on the initial criteria regarding imagequality, costs, power dissipation, etc. During operation, the optimizermodule 100 communicates with the video processing system to change levelsettings in the video processing system 115 of parameters, controls,etc., which in turn causes the video output to be modified. Themodification of the video output, in turn, may cause the video qualityevaluation module 121 to change. As the video quality is evaluated, thisevaluation is provided to the MOGA 105 of the optimizer module 100,wherein the MOGA will provide for further changes in level settings andparameters until the video quality evaluation module 121 provides theoptimum evaluation (taking into account, of course, the other previouslydiscussed competing factors considered by the optimize module). Thevideo quality evaluation module may evaluate a video stream incomponents, for example, with an overall quality rating and/or one forthe audio, video and text portions of the stream. In this way, the MOGA105 can prioritize which portion of the stream should quality bemaintained if there is a requirement to cut back on some transmissionsettings.

For example, during a conference call on a G3 cellphone, the videoquality may be lower in priority than audio quality, so at times thevideo quality would be sacrificed rather than lose the audio portion ofthe conference call. This change in quality may be caused by networkcongestion, sudden reduction in available bandwidth, reduced poweravailability, changed storage requirements, etc.

The MOGA permits good solutions (high signal quality, e.g. with video,and crisp clear video stream) with a high level of confidence. Themulti-objective search engine has the capability to change the structureand the parameters of the signal processing system under study and itsassociated level of performance is reported back to the multi-objectiveengine to help formulating better search points.

FIG. 3 is a flowchart providing explanation of an aspect of the presentinvention. At step 300, a video stream is received by the videoprocessing system 115.

At step 305, the system, which preferably has initial default settingsfor controls and parameters, so that color improvement by the module 117and sharpness enhancement and noise cancellation are output to the videoquality evaluation module 121.

At step 310, the video quality evaluation module evaluates the quality,and reports the evaluation to the video optimizer 100 (particularly theMOGA 105). The quality evaluation is preferably according to scalableobjective metrics referred to in the previously incorporated byreference U.S. patent application Ser. No. 09/734,823, but it is not anabsolute requirement of the present invention. The video qualityevaluation module 121 provides the quality evaluation to the MOGA of thevideo optimizer 100.

At step 315, the MOGA analyzes the video quality evaluation, and inconjunction with receipt of the level settings of parameters of thevideo processing system, and the costs, time and other factors,processes the information using genetic algorithms and provides settingsof control parameters to the video processing system 115.

At step 320, it is determined whether or not the video stream has endedor there is another video stream. If there are more evaluations from thevideo quality evaluation module 121, the MOGA will evaluate the criteria(such as control settings, video quality, etc.) and update the levelsettings of parameters and controls. It is also possible for the MOGA tore-evaluate at intervals, such as every other stream, for example, oreven more than once per stream, assuming that computing power is fastenough to make such re-evaluations at fractions of a stream.

While several aspects of the present invention have been illustrated anddescribed, it will be understood by those skilled in the art thatvarious changes and modifications may be made, and equivalents may besubstituted for elements thereof without departing from the true scopeof the present invention. In addition, many modifications may be made toadapt to a particular situation and the teaching of the presentinvention without departing from the central scope of the claimedinvention.

For example, when the statistical analyzer is interpolating factors suchas “time to market” the system is more of a prototype than a finishedconsumer product, as it allows engineers to view different imagequalities without just varying costs, but market timing. However, thesystem can also be a practical system that functions in, for example,conferencing equipment at a retail level. In such a scenario, users maybe able to prioritize various factors that have an overall effect onimage quality. Therefore, it is intended that the present invention notbe limited to the particular embodiment disclosed as the best modecontemplated for carrying out the present invention, but that thepresent invention include all embodiments falling within the scope ofthe appended claims.

1. A decision support system for a signal processing system comprising:a video processing system 115 for receiving and processing a videostream and providing a video output; a video quality evaluation module121 that receives the video output from the video processing system andevaluates the quality according to predetermined criteria; a videooptimizer 100 adapted for receiving the evaluated quality of the videooutput from evaluation module 121 and level settings of parameters andfor setting controls of the levels settings of parameters of videoprocessing system 115, said video optimizer including a Multi ObjectiveGenetic Algorithm (MOGA) engine, wherein said MOGA uses geneticalgorithms to optimize the settings of controls for video processingsystem 115 to optimize quality at a predetermined level.
 2. The systemaccording to claim 1, wherein said optimizer 100 includes a statisticalanalyzer 110 that associates at least one item with setting controls ofthe level settings of parameters of video processing system 115 toreceive a certain image quality evaluated by quality evaluation module121.
 3. The system according to claim 2, wherein said at least one itemincludes manufacturing costs.
 4. The system according to claim 3,wherein the manufacturing costs include time to market.
 5. The systemaccording to claim 2, wherein said at least one item analyzed by thestatistical analyzer 110 includes bandwidth availability.
 6. The systemaccording to claim 2, wherein said at least one item analyzed by thestatistical analyzer 110 includes network availability.
 7. The systemaccording to claim 1, wherein the image quality evaluation moduleevaluates quality of multimedia according to video, audio and text, andthe MOGA 105 includes prioritizing instructions so as to prioritize thequality of audio, video and text components of a video stream.
 8. Thesystem according to claim 8, wherein the system comprises a telephonewith video capability and the quality of the audio portion has thehighest priority.
 9. The system according to claim 8, wherein the systemcomprises a conference call system and the quality of the video portionhas the highest priority.
 10. The system according to claim 8, whereinthe quality of audio is prioritized according to network congestion. 11.The system according to claim 8, wherein the quality of audio isprioritized according to bandwidth availability.
 12. The systemaccording to claim 8, wherein the quality of audio is prioritizedaccording to power dissipation.
 13. A method for a decision supportsystem for a signal-based processor, comprising the steps of: (a)receiving a video-processing stream by a video processing system s300;(b) processing color, sharpness and noise cancellation according toinitial default settings and parameters s305; (c) evaluating videoquality by an objective video quality evaluation module s310; (d) usinga video optimizer to provide level settings and control parameters forthe video processing system based on feedback of quality from the videoquality evaluation module; (e) determining s320 whether the video streamhas ended, and repeating steps (c) through (e) until the video streamhas ended.
 14. The method according to claim 14 wherein step (e) furtherincludes determining whether any additional video streams requireprocessing.
 15. The method according to claim 14, wherein the optimizerin step (a) includes a MOGA engine and a statistical analyzer.
 16. Themethod according to claim 15, wherein the statistical analyzer willanalyze at least one item associated with setting controls of the levelsettings of parameters of the video processing system to receive acertain image quality evaluated by a quality evaluation module.
 17. Themethod according to claim 16, wherein the at least one item comprises aplurality of items and includes bandwidth availability, and thestatistical analyzer interpolates the items.
 18. The method according toclaim 16, wherein the at least one item comprises a plurality of itemsand includes power dissipation, and the statistical analyzerinterpolates the items.
 19. The method according to claim 16, whereinthe at least one item comprises a plurality of items and includesnetwork availability, and the statistical analyzer interpolates theitems.
 20. The method according to claim 16, wherein the at least oneitem comprises a plurality of items and includes time to market, and thestatistical analyzer interpolates the items.